hackernews

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by noting the default model is free and that Anthropic probing requires a user-provided API key with direct billing. It also describes the return structure (per-model score, confidence, signals, raw_response) beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, each earning its place: purpose, default vs. opt-in models, return structure, and use cases. No redundant wording; information is front-loaded and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 4 parameters and no output schema, the description covers purpose, usage, parameters, and return structure. It mentions a 'combined view' but does not detail its structure, which is acceptable given the tool's simplicity and lack of output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds examples for 'entity' (e.g., 'Pipeworx'), clarifies 'models' values and default behavior (omit for workers-ai), and explains '_apiKey' is passed through. It provides practical guidance beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes LLMs for knowledge about an entity and returns a visibility score per model, with specific verb 'probe', resource 'LLMs', and output 'score (0-100)'. It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on general brand awareness rather than competitor-specific analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage contexts: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It explains the default model and how to use Anthropic with a BYO key. However, it does not explicitly state when not to use the tool or compare it to siblings like 'scan_competitor_ai_presence'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool selects data sources and fills arguments automatically, and it handles natural language queries. However, it lacks details on limitations (e.g., supported topics, error handling, or rate limits), which are important for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core functionality, followed by benefits and examples. Every sentence adds value: the first explains the tool's purpose, the second details its automation, and the third provides concrete use cases. It is appropriately sized with zero wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (natural language processing with automatic tool selection), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, usage, and parameter semantics well. However, it could improve by mentioning potential limitations or output format, which would help an agent anticipate results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the parameter's purpose: 'Your question or request in natural language' and providing examples that illustrate the expected format and scope. This enhances understanding beyond the schema's basic type and requirement.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus direct tool invocation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives (implicitly, use other tools for specific operations) and includes examples ('What is the US trade deficit with China?', etc.) that illustrate appropriate use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint, openWorldHint, and non-destructive. The description adds that it classifies bets, fans out to relevant data packs, and returns a comparison, providing complete behavioral context without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: it starts with purpose, then process, then use cases. While slightly verbose, every sentence adds value and it front-loads the core function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description thoroughly explains the return value (evidence packet + comparison). It covers inputs, processing logic, and purpose, fully addressing the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters. Description adds meaning by explaining that 'market' accepts slugs, URLs, or question text, and that 'depth' defaults to thorough. This enriches the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool researches Polymarket bets by pulling Pipeworx data. It names specific inputs (slug, URL, question text) and outputs (evidence packet, market-vs-model comparison), clearly distinguishing it from generic research tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage scenarios like 'should I bet on X?' and 'what does the data say...', and notes it's the core demo product. It lacks explicit when-not-to-use but gives strong context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses returned data fields and URI generation for both entity types, but no annotations are provided. Missing details on side effects, permissions, or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no redundancy; front-loaded with purpose and constraints. Efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key aspects: entity types, data returned, and URI provision. Lacks output schema specification but sufficient for an agent to decide to use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters with descriptions; description adds concrete data fields and examples, enriching meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it compares 2-5 entities, specifies two entity types (company and drug) with data fields for each, and notes efficiency gain over sequential calls. Distinguishes from siblings like resolve_entity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies use for multi-entity comparison and mentions replacing 8-15 sequential calls, but does not explicitly state when not to use or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'the most relevant tools with names and descriptions,' which adds context about the output format. However, it lacks details on behavioral traits such as performance characteristics, error handling, or any limitations beyond the scope mentioned. The description does not contradict any annotations, as there are none.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines. Every sentence earns its place by providing essential information without redundancy, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a search function with 2 parameters) and the absence of annotations and output schema, the description is reasonably complete. It covers the tool's purpose, usage context, and output format, but could benefit from more details on behavioral aspects or error handling. However, it adequately supports agent understanding for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') with descriptions. The description adds minimal value beyond the schema by implying the 'query' parameter is for natural language searches, but it does not provide additional semantics or usage examples not covered in the schema. Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by emphasizing its role in discovering tools among 500+ options. It explicitly mentions what it returns ('most relevant tools with names and descriptions'), making the purpose distinct and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This includes a clear condition (500+ tools) and a specific scenario (finding the right tools for a task), effectively differentiating it from alternatives without naming them directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. It states the tool returns citation URIs and replaces 10-15 sequential calls, implying efficiency. It doesn't mention auth needs, rate limits, or response structure, but given the lack of output schema, it is fairly transparent about what to expect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences) and front-loaded with the main purpose. It efficiently conveys the tool's value and constraints without extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only 2 parameters, no output schema, and no annotations, the description is complete. It clearly explains what the tool returns (six specific data sources with citation URIs) and provides usage guidance for edge cases (names not supported, federal contracts).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds meaning beyond the schema: it explains that only 'company' is supported for type and that value can be a ticker or CIK (not names). This helps the agent understand parameter constraints and use resolve_entity for names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides a 'full profile of an entity across every relevant Pipeworx pack in one call', listing specific data sources (SEC filings, revenue, patents, news, LEI) for type='company'. It distinguishes itself from sibling tools by noting that for federal contracts, usa_recipient_profile should be called directly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use (company entities) and when not (federal contracts, name not supported). It provides an alternative: use resolve_entity first if only have a name. This gives clear guidance for the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Delete' implies a destructive mutation, but fails to mention critical details like permissions required, whether deletion is permanent or reversible, error handling, or rate limits. This leaves significant gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It is front-loaded with the core action ('Delete') and resource, making it immediately clear and appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permanence, side effects), usage context, and return values, which are essential for safe and effective tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'key' parameter documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format examples or constraints. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Delete') and resource ('a stored memory by key'), making the purpose specific and understandable. However, it doesn't distinguish this tool from potential siblings like 'recall' or 'remember' in the memory management context, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as 'recall' (which might retrieve memories) or 'remember' (which might store them). It lacks context about prerequisites, exclusions, or explicit alternatives, leaving usage decisions ambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context: fetching the page, extracting content, and emitting markdown format, which complements the annotations without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences plus a bullet list of uses. It is efficiently structured and front-loaded, though the list format slightly reduces flow.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters, full schema coverage, and rich annotations, the description covers the purpose, behavior, and usage context adequately. It explains output format and deployment, meeting completeness needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions, so the baseline is 3. The main description does not add parameter specifics beyond what the schema provides, but it's adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Generate') and resource ('llms.txt file'), and explains the purpose clearly for AI crawlers. It distinguishes itself from sibling tools like 'ai_visibility_check' by focusing on producing a standard file.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases ('getting a client's site indexed', 'drafting for your project', 'auditing competitor') but does not mention when not to use or alternatives, which would improve it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates a read-only operation ('Get') and specifies the item types ('story or comment'), but doesn't mention potential errors (e.g., for invalid IDs), rate limits, or authentication needs. It adds basic context but lacks detailed behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the purpose without unnecessary words. Every part earns its place by specifying the action, resource, type, and identification method concisely.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (single parameter, no annotations, no output schema), the description is adequate but has gaps. It covers the basic purpose and parameter intent but lacks details on return values, error handling, or usage nuances, making it minimally viable rather than fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the parameter 'id' fully documented in the schema. The description adds minimal value by reiterating 'numeric ID' but doesn't provide additional semantics like ID ranges or examples. Baseline 3 is appropriate as the schema handles most parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get'), resource ('a single Hacker News item'), and scope ('story or comment') with precise identification method ('by its numeric ID'). It distinguishes from sibling tools like 'get_top_stories' (which retrieves multiple stories) and 'search_hn' (which searches content).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'by its numeric ID,' suggesting this tool is for retrieving known items rather than discovering or searching. However, it doesn't explicitly state when not to use it or name alternatives like 'search_hn' for unknown IDs, leaving some guidance implicit rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches current top stories but doesn't mention any behavioral traits such as rate limits, authentication needs, data freshness, or potential side effects. This leaves significant gaps in understanding how the tool behaves in practice.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that directly states the tool's function without any unnecessary words. It's front-loaded with the core purpose, making it highly efficient and easy to parse, with every word earning its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't address what the tool returns (e.g., story details, format), error conditions, or behavioral aspects like performance or limitations. For a tool with no structured metadata, this minimal description leaves too many contextual gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'count' parameter documented as 'Number of top stories to return (default: 10)'. The description doesn't add any meaning beyond this, such as explaining what 'top stories' entails or constraints on the count value. Given the high schema coverage, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get') and resource ('current top stories from Hacker News'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_item' or 'search_hn', which could provide similar content through different mechanisms, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_item' or 'search_hn'. It lacks context about scenarios where top stories are preferred over search results or specific item retrieval, leaving the agent to infer usage without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries burden. Discloses rate limit and that it's free. Does not specify post-call behavior (e.g., confirmation, no response, async handling). Lacks detail on whether feedback is stored or how it's processed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: 4 sentences covering purpose, usage, and rate limit with no wasted words. Front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers input usage well given 3 parameters and nested object. No output schema, but description does not explain return behavior (if any). Missing details on response or error handling, which could be important for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with good parameter descriptions. The description adds a guideline about message content (not including verbatim prompt) but does not add meaning beyond the schema's enum descriptions. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description explicitly states the tool is for sending feedback to the Pipeworx team, listing specific use cases (bug reports, feature requests, missing data, praise). Clearly distinguishes from sibling tools like ask_pipeworx (Q&A) or discover_tools (tool discovery).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear guidance on what to include (describe in terms of Pipeworx tools/data) and what to exclude (end-user prompt verbatim). Mentions rate limit (5 per day). Does not explicitly contrast with siblings, but the purpose is distinct enough to imply when to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond annotations by disclosing that the data is self-aggregating, derived from CF analytics-engine, no PII, and cached 5min-1h. Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable behavioral context without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that is front-loaded with the main result and uses bullet points for use cases. Every sentence adds value, and there is no wasted text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one optional parameter, no output schema, comprehensive annotations), the description is fairly complete. It covers the return content (top tools, top packs, total call volume) and cache behavior. It does not detail the output structure, but that is acceptable without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with a detailed description of the 'window' parameter. The description does not add additional meaning beyond what the schema already provides, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool does: returns top tools, top packs, and total call volume over a recent window. It uses specific verbs and resources, and distinguishes itself from sibling tools by mentioning 'What other AI agents are calling on Pipeworx right now.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides three explicit use cases: discovering hot data sources, confirming canonical tools, and checking alignment with other agents. It implies usage context well but does not explicitly state when not to use or name alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and openWorldHint, but the description adds significant detail: the tool walks child markets, extracts dates/thresholds, sorts them, and reports violations. This goes beyond annotations, fully disclosing the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that efficiently conveys the concept, usage, and output. While there is no wasted text, a slight structural improvement (e.g., bullet points for the return format) could enhance readability. Currently good, not excellent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and lack of output schema, the description is remarkably complete. It explains the input, the process (extracting, sorting, checking), and the exact output format (list of arbitrage pairs with fields). Combined with annotations, this fully informs the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the description's job is minimal. The description does not add semantic meaning beyond what the schema already provides (event slug/URL). Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: detecting arbitrage via monotonicity violations in Polymarket events. It specifies the resource (event with multiple date/threshold markets) and the verb (find arbitrage opportunities), leaving no ambiguity. The unique concept distinguishes it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool: when checking events with ordered date/threshold markets. It specifies the input format (slug or URL). However, it does not explicitly mention when not to use it or provide alternatives, though the context makes usage clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral details: it scans top markets, groups by asset, fetches price history once, computes model probability using a lognormal model from FRED and live coinpaprika price, and ranks by edge magnitude. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (5 sentences) and front-loaded with the core purpose. Every sentence adds value: the first sentence states the main goal, the second explains the model, the third details the process, the fourth describes the output, and the fifth gives the use case. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and 3 parameters, the description provides sufficient context: it explains the input parameters implicitly, describes the internal process, and states the output includes top N markets ranked by edge magnitude with suggested trade direction. It slightly lacks details on return format (e.g., exact fields) but is complete enough for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (all 3 parameters have descriptions). The overall description mentions 'top N' related to limit and 'volume window' related to window, but adds no additional semantic meaning beyond what the schema already provides. Baseline score of 3 is appropriate as the description does not significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: scanning high-volume Polymarket markets to find those with largest disagreement between Pipeworx data and market price. It specifies the verb 'Scan', resource 'Polymarket markets', and outcome 'return ones where ... disagrees most'. It also mentions it's for the 'what should I bet on today' question, making the purpose highly specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states the tool is built for 'what should I bet on today' and helps discover opportunities without manual paging. While it does not explicitly state when not to use or compare with sibling tools like polymarket_arbitrage, the context is clear enough for an agent to determine appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: retrieving or listing memories based on the presence of the 'key' parameter, and specifies that memories can be from current or previous sessions. However, it doesn't mention potential limitations like memory size, retrieval speed, or error handling for non-existent keys, which would be useful for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise and well-structured in two sentences. The first sentence states the purpose and usage, while the second provides contextual guidance. Every word earns its place with no redundancy or fluff, making it easy for an AI agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single optional parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and context effectively. However, it lacks information about return values (e.g., format of retrieved memories or listed keys) and doesn't mention any error conditions or limitations, which would be helpful since there's no output schema to provide this information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents the single parameter 'key' with its description. The description adds value by explaining the semantic behavior: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' This clarifies the dual functionality based on parameter presence, going beyond the schema's technical description. However, it doesn't provide additional details like key format or examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory by key', 'all stored memories'). It distinguishes this from sibling tools by explicitly mentioning it retrieves context saved earlier in the session or previous sessions, which differentiates it from tools like 'get_item' or 'search_hn' that likely retrieve different types of data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Retrieve a previously stored memory by key, or list all stored memories (omit key).' It also specifies the context: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' This clearly tells the agent both the primary use case and the alternative (listing all memories when key is omitted), with no misleading information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses key behaviors: parallel fan-out to SEC EDGAR, GDELT, USPTO; accepted date formats; return structure (structured changes, count, URIs). However, it omits details like idempotency, required permissions, or potential latency, which an agent might need.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that efficiently covers purpose, behavior, parameter details, and use cases. Every sentence adds value without redundancy or verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given moderate complexity (3 required params, no nested objects, no output schema), the description adequately explains input, behavior, and return format. It lacks some context like error handling or limits, but covers the essentials for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context: explains the 'type' enum constraint (only company), gives recommended values for 'since' (e.g., '30d'), and clarifies 'value' as ticker or CIK. This adds practical guidance beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves recent changes for an entity since a given time. It specifies the entity type (company) and the behavior (fanning out to multiple sources). This distinguishes it from sibling tools like compare_entities or entity_profile.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly suggests use cases: 'brief me on what happened with X' and change-monitoring workflows. It does not explicitly list when NOT to use it or mention alternative tools, but the provided usage context is clear enough for an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool stores data persistently for authenticated users versus temporarily for anonymous sessions (24 hours), and it supports cross-tool context. It does not mention rate limits, error conditions, or data format constraints, but covers essential operational context adequately.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence adds value without redundancy, and it is appropriately sized for the tool's complexity. No words are wasted, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete: it explains the purpose, usage, and key behavioral traits like persistence differences. However, it lacks details on return values (since no output schema) and potential errors, leaving minor gaps. It compensates well but not fully for the absence of structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters ('key' and 'value') with examples. The description adds no additional parameter semantics beyond what the schema provides, such as constraints or usage nuances. It meets the baseline for high schema coverage but does not enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'recall' (likely for retrieval) and 'forget' (likely for deletion). It provides concrete examples of what can be stored ('intermediate findings, user preferences, or context across tool calls'), making the purpose unambiguous and well-differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), which helps guide the agent. However, it does not explicitly state when not to use it or name alternatives (e.g., 'recall' for retrieval), missing full differentiation from siblings. The guidance is practical but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the core behavior (resolution and return of canonical IDs) and input/output formats, but does not disclose potential side effects, error handling, idempotency, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the main purpose, and contains no unnecessary words. It efficiently conveys all key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 2-parameter tool with no output schema, the description covers inputs, outputs, and the value proposition (replacing multiple calls). It lacks details on error handling or edge cases, but is largely complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage, so the baseline is 3. The description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying the input formats beyond the schema, making it easier for the agent to formulate valid calls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool resolves entities to canonical IDs, specifies the supported type (company) and input formats (ticker, CIK, name), and lists outputs (ticker, CIK, company name, URIs). It distinguishes itself from sibling tools by consolidating multiple lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the tool replaces 2-3 lookup calls and explicitly limits v1 to company entities, providing clear context for when to use it. It doesn't specify alternatives for other entity types, but the constraint is clearly communicated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds value by explaining internal call to ai_visibility_check, per-entity probing, and ranking logic. No contradiction; description enriches behavioral understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: first states main purpose, second details the process, third gives use case. No extraneous information. Front-loaded with the key action. Efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 4 parameters and no output schema, the description explains input semantics, internal operation, and output shape (ranked list with score/confidence/signal density). Could mention return type explicitly (e.g., JSON array), but overall sufficiently complete given annotations and schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions. Description adds extra meaning: 'entities' first entry treated as subject, models selection condition, apiKey only if 'anthropic' in models. Provides clarification beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Compare', 'Probes'), resource ('AI visibility across multiple entities'), and output ('ranked list... most/least recognized'). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (different type).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Useful for competitive AI-marketing audits' and gives an example question. It implies when to use but does not explicitly state when not to use or mention alternatives like ai_visibility_check for single entity checks. Clear usage context without exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions the Algolia API, hinting at external dependencies, but doesn't disclose rate limits, authentication needs, error handling, or response format. For a search tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It front-loads the core purpose ('Search Hacker News stories') and adds clarifying details ('and other content types', 'using the Algolia search API') concisely. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a search operation with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list of stories with fields), potential limitations, or error cases. For a tool with 3 parameters and external API reliance, more context is needed to guide effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain search syntax or result ordering). Baseline 3 is appropriate when the schema handles parameter documentation effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Search') and resource ('Hacker News stories (and other content types)'), and mentions the underlying API ('Algolia search API'). It distinguishes from 'get_item' (specific item retrieval) and 'get_top_stories' (predefined ranking) by focusing on query-based search. However, it doesn't explicitly contrast with siblings, keeping it at 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'get_item' or 'get_top_stories'. It lacks context about scenarios where search is preferable (e.g., finding specific content vs. browsing top stories) or any prerequisites. This leaves the agent without explicit usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full responsibility. It discloses the tool's scope (company-financial, US public companies), underlying sources (SEC EDGAR + XBRL), and output types (verdict, structured form, actual value, citation, delta). It also reveals that the tool is a composite operation replacing 4–6 sequential calls. While it does not address auth or rate limits, this is acceptable for the domain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, each sentence delivering essential information. It front-loads the main purpose in the first sentence and adds supporting details in the second. Every phrase earns its place without repetition or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (fact-checking with multiple internal steps) and the absence of an output schema, the description does a good job explaining what the tool returns (verdict types, structured form, actual value, citation, delta). It could mention potential error conditions or performance characteristics, but the current level is adequate for an agent to decide if the tool fits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter `claim` has a good schema description with examples. The tool description adds valuable context beyond the schema by specifying that the claim must be a company-financial claim for a public US company, narrowing the domain. With 100% schema coverage, the description enhances understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's core function (fact-checking natural-language claims against authoritative sources) with a specific domain (company-financial claims for public US companies) and a detailed list of returned verdicts. It distinguishes itself from sibling tools like `resolve_entity` or `entity_profile` by focusing on claim validation with a multi-step composite operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool (for fact-checking claims, specifically company-financial claims) and notes that it replaces multiple sequential agent calls. However, it does not explicitly state when not to use it (e.g., for non-financial claims) or mention alternative tools for other domains.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.